Summary of Improving 3d Finger Traits Recognition Via Generalizable Neural Rendering, by Hongbin Xu et al.
Improving 3D Finger Traits Recognition via Generalizable Neural Rendering
by Hongbin Xu, Junduan Huang, Yuer Ma, Zifeng Li, Wenxiong Kang
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel approach to 3D finger biometric recognition using neural radiance fields (NeRFs) and neural transformers. The existing explicit 3D pipeline reconstructs models first and then extracts features, but this approach suffers from information dropping and hardware-algorithm coupling. The authors question whether explicit 3D reconstruction is necessary for recognition tasks and propose an implicit method, FingerNeRF, that utilizes NeRFs to learnable networks. The model incorporates extra geometric priors based on finger traits like fingerprints or veins using a novel Trait Guided Transformer (TGT) module. The authors evaluate their approach on three datasets: SCUT-Finger-3D, SCUT-FingerVein-3D, and UNSW-3D, achieving superior results compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to recognize people’s fingers using special fields that learn from neural networks. It’s like a superpower for identifying who someone is just by looking at their fingers! The old method was complicated and needed special hardware, but this new approach is simpler and can work on different types of devices. The authors tested it on three groups of finger images and found that it works really well. |
Keywords
» Artificial intelligence » Transformer
